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What sidetracked many people, Hickey says, was the belief that the eight-member Federal Reserve (Fed) was omnipotent When the Fed cut rates, the market celebrated People were euphoric, believing a rate cut would save them On TV, financial commentators were hooting and hollering, calling for another rate cut, Hickey remembers They thought the stock market would be immune As long as the Fed was there, they believed the stock market would rally When the Fed surprised the market by cutting rates again, the same commentators called it a miracle, a gift from heaven, and the greatest central bank on earth One CNBC commentator was telling people to put their money into the Four Horsemen, including Google, at over $700 a share When you write BUY GOOG on your knuckles, that should give you some sense we re in trouble, he quips According to Hickey, many pros depend on the individual investors to fuel rallies Some of the antics you see are designed to lure greater fools with new money and short memories into the market Meanwhile, after the Fed cut rates, Google went even higher, even though it had missed its July numbers It missed its numbers because Internet traffic had started to slow, Hickey says And the biggest advertisers were from mortgage companies, who were going to get killed And yet, they were on TV telling people to buy these high-priced tech stocks even after the fundamentals were rolling over Hickey is not a big fan of the Fed, which he believes has been irresponsible These are mostly Ivy League educated economists, but they never get it right, he says Fed chairman Greenspan said it wasn t possible to identify bubbles, and yet there was this gigantic housing bubble that was obvious not just to me but to many people Economists came out and said not to worry because the Fed had cut rates from 6 percent to 1 percent But it didn t matter in the end He believes that the Fed members keep getting it wrong because they rely too heavily on their economic models, which never seem to come up with the right answer It s common sense versus economic modeling, Hickey says I was hitting my head against the wall Why couldn t they see bubbles imploding all over.

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Table 52: Computational and memory requirements for the recursive least squares algorithm of eqns (537) and (540) with x Rn , Hm+1 R1 n , y R, and Rm+1 R The matrix Fm+1 Rn n represents the information matrix

role in determining the amount of required computation Section 54 will show that eqns (537) and (540) are equivalent to Km+1 xm+1 Pm+1 = = = Pm Hm+1 Rm+1 + Hm+1 Pm Hm+1 xm + Km+1 ( m+1 Hm+1 xm ) y (I Km+1 Hm+1 ) Pm

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the place Meanwhile, the Fed was supplying all this liquidity into the market by cutting rates lower and lower Hickey saw many similarities between 2007 and 2000 According to market lore, you re not supposed to fight the Fed, but I knew it was time History told me that The Fed was not omnipotent in 1930, they weren t omnipotent in 2000, and they weren t omnipotent in 2007 I think it s very important to understand history These patterns do reoccur, but not always in the same way They are always a little different And just as in the past, people thought he was insane for fighting the Fed It wasn t just the crazies taunting me on the phone and singing songs, he says Legitimate people also called me and said, The Fed will never allow the stock market to fall because the consequences will be so awful Stocks are destined to go higher The Fed is too powerful Resistance is futile

(541) (542) (543)

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The computational requirements for this set of equations are evaluated in Table 53 The workspace and computational requirements are signi cantly reduced relative to the previous algorithms In addition to requiring smaller amounts of memory and computation, the recursive least squares algorithm provides iterative estimates immediately following the measurement time This has the potential to provide estimates with reduced delay over an approach which waits to accumulate a xed sized batch of m samples before calculating an estimate Example 53 This example reconsiders the problem stated in Example 51 using the algorithm of eqns (541 543) For this problem, n = 1, Hm+1 = 1 and Rm+1 = 2 Using the rst measurement and eqns (529 530) to initialize the state and covariance, we have x1 = y1 and P1 = 2 The recursion then starts at m = 2: Km+1 Pm+1 Pm 2 + Pm = (1 Km+1 ) Pm =

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